Centralized hospital monitoring system for automatically detecting upper airway instability and for preventing and aborting adverse drug reactions

ABSTRACT

A system and method for the automatic diagnosis of obstructive sleep apnea in a centralized hospital critical care monitoring system for the monitoring of a plurality of patients in at least one of a critical care, step down, and cardiac ward by telemetry. The system includes a central processor having a display, and a plurality of telemetry units for mounting with patients, each of the telemetry units has a plurality of sensors for connection with each patient, the telemetry unit is capable of the transmission of multiple signals derived from the sensors to the central processor, in one preferred embodiment the method comprising steps of programming the system to analyze the signals and to automatically identify the presence and severity of obstructive sleep apnea and to provide an indication of the identification.

This application claims priority of prior application Ser. No.11/369,355, filed Mar. 7, 2006; application Ser. No. 10/150,582, filedMay 17, 2002; provisional applications 60/291,692 and 60/291,687, bothfiled May 17, 2001 and provisional application 60/295,484 filed Jun. 10,2001, the disclosures and contents of each of which is incorporated byreference as if completely disclosed herein.

FIELD OF THE INVENTION

This invention relates to centralized hospital monitoring systems andparticular to the organization, analysis, and automatic detection ofpatterns indicative of upper airway instability during sleep, deepsedation, and analegia.

BACKGROUND AND SUMMARY OF THE INVENTION

The high number of unnecessary deaths in the hospital due to errorsrelated to pharmaceutical administration such as sedative and narcoticshas been a recent focus of US government studies and much discussion inthe literature and press. The present inventors recognized that theseadverse events occur not only due to improper dosage of medications orthe administration of drug to the wrong patient, as has been recentlyhighlighted in the medical literature and press, but also due to failureto recognize complex patterns along monitored outputs (such as thoseshown in FIG. 2) indicative of complex patterns along monitored outputs(such as those shown in FIG. 2) indicative of patient instabilitybefore, during, and after the administration of such medications. Thesepatterns can provide evidence that a given dose of medication, which mayappear to be correct according to the Physician's Desk Reference orother source, may be too much for a given patient in a given physiologicstate. Administration of standard acceptable dosages” to patients withpotentially unstable physiology can produce an insidious and deadlyoccurrence of relative drug excess, which will not be prevented bysimple computer matching of patient name and drug. Further, the presentinventors recognized that failure to timely interrupt infusion upon theoccurrence of physiologic instability represented a major cause ofdeath. The timely recognition of a change in the pattern of the patientmonitored output can be seen as the last opportunity to correct themistake of wrong drug, wrong dose, wrong patient, relative drug excess,or a potentially fatal idiosyncratic or allergic reaction.

In hospitals, throughout the United States monitored patients areexperiencing profound physiologic instability before and duringmedication infusion producing patterns as shown in FIG. 2 and yet stillare being subjected to continuous infusion of further destabilizing andpotentially deadly narcotics and sedation simply because the hospitalmonitors do not recognize the patterns nor are they programmed to warnthe hospital worker or to lock out the infusion based on suchrecognition. An example of such instability and a system and methodaccording to the present invention for identification of such patternsfollows although, upon this teaching, the skilled artisan will recognizethat there are many modifications within the scope of this teaching,which will allow the recognition of other patterns of instability.

A major factor in the development of respiratory failure (one of themost common causes of death in the hospital) is airway instability,which results in airway collapse during sedation, stroke, narcoticadministration, or stupor. As illustrated in FIGS. 3 a and 3 b, suchcollapse occurs in dynamic cycles called airway instability clustersaffecting a range of physiologic signals. Subgroups of patients in thehospital are at considerable risk from this type of instability. Inaddition patients with otherwise relatively stable airways may haveinstability induced by sedation or narcotics. The present inventorsrecognized that it is critical that this instability be recognized inreal time in the hospital so that the dose can be adjusted or the drugwithheld upon the recognition of this development. They also realizedthat it is critical to use the opportunity afforded by hospitalizationin association with hospital monitoring to automatically evaluate forthe common disorder induced by upper airway instability-obstructivesleep apnea. Conventional central patient monitors are neitherconfigured to provide interpretive recognition the cluster patternsindicative of airway and ventilation instability nor to provideinterpretative recognition of the relationship between and along airwayinstability clusters. In fact, such monitors often apply averagingalgorithms, which attenuate the clusters. For these reasons thousands ofpatients each day enter and leave hospital-monitored units withunrecognized sleep apnea and ventilation and airway instability.

This failure of conventional hospital based patient monitors to timelyand/or automatically detect cluster patterns indicative of airwayinstability can be seen as a major health care deficiency indicative ofa long unsatisfied need. Because obstructive sleep apnea, a conditionderived from airway instability, is so common, the consequence of thefailure of conventional hospital monitors to routinely recognize upperairway instability clusters means that many of patients with thisdisorder will never be diagnosed in their lifetime. For these patients,the diagnostic opportunity was missed and the health implications andrisk of complications associated with undiagnosed airway instability andsleep apnea will persist in this group throughout the rest of theirlife. A second group of patients will have a complication in thehospital due to the failure to timely recognize airway instability.Without recognition of the inherent instability, a patient may beextubated too early after surgery or given too much narcotic (the rightdrug, the right patient, the ordered dose but unknowingly a “relativedrug excess”). Indeed until clusters indicative of airway instabilityare routinely recognized by hospital monitors, the true incidence ofrespiratory failure, arrest, and/or death related to the administrationof IV . . . sedation and narcotics to patients in the hospital withairway instability will never be known but the number is probably in thetens of thousands each year and airway instability is just one exampleof the types of physiologic instability which are not automaticallycharacterized by central hospital systems.

To understand the criticality of recognizing airway instability inreal-time it is important to consider the significance of the combinedeffect that oxygen therapy and narcotics or sedation may have in thepatient care environment in the hospital, for example, in the managementof a post-operative obese patient after upper abdominal surgery. Such apatient may be at particular risk for increased airway instability inassociation with narcotic therapy in the through 3rd post-operative daydue to sleep deprivation, airway edema, and sedation. Furthermore, inthe second and third postoperative day monitoring the vigilance ofhospital personnel may diminish due to perceived stability, and reboundrapid eye movement (REM) sleep which can increase upper airwayinstability may occur due to antecedent sleep deprivation. Indeed, manyof these patients have significant sleep apnea prior to admission to thehospital which is unknown to the surgeon or the anesthesiologist due tothe subtly of symptoms. Such patients, even with severe sleep apnea, arerelatively safe at home because of an intact arousal response; however,in the hospital, narcotics and sedatives often remove this “safety net.The administration of post-operative narcotics can shift the arousalcurve to the right and this can significantly increase the danger ofairway instability and, therefore, place the patient at substantialrisk. Many of these patients are placed on electrocardiographicmonitoring but the alarms are generally set at high and low limits.Hypoxemia, induced by airway instability generally does not generallyproduce marked levels of tachycardia; therefore, airway instability ispoorly identified by simple electrocardiographic monitoring without theidentification of specific pattern of clusters of the pulse rate. Inaddition, simple oxiretry evaluation is also a poor method to identifyairway instability. Conventional hospital oximeters often have averagingintervals, which attenuate the dynamic desaturations. Even when theclustered desaturations occur they are often thought to represent falsealarms because they are brief. When desaturations are recognized aspotentially real this often results in the simple and often misguidedaddition of nasal oxygen. However, nasal oxygen may prolong the apneasand potentially increase functional airway instability. From amonitoring perspective, the addition of oxygen therapy can be seen topotentially hide the presence of significant airway instability byattenuation of the level of desaturation and reduction in theeffectiveness of the oximeter as a monitoring tool in the diagnosis ofthis disorder.

Oxygen and sedatives can be seen as a deadly combination in patientswith severely unstable airways since the sedatives increase the apneasand the oxygen hides them from the oximeter. For all these reasons, aswill be shown, according to the present invention, it is critical tomonitor patients with increased risk of airway instability for thespecific monomorphic and polymorphic cluster patterns as will bediscussed, during the administration of narcotics or sedatives.

Having identified, supra, the long and critical need, a discussion ofthe background physiology of upper airway instability will first beprovided.

The central drive to breath, which is suppressed by sedatives ornarcotics, basically controls two critical muscle groups. The upperairway “dilator muscles” and the diaphragm “pump muscles”. The tone ofboth these muscle groups must be coordinated. A fall in afferent outputfrom the brain controller to the airway dilators results in upper airwaycollapse. Alternatively, a fall in afferent output to the pump musclescauses hypoventilation.

Two major factors contribute to respiratory arrest in the presence ofnarcotic administration and sedation. The first and most traditionallyconsidered potential effect of narcotics or sedation is the suppressionby the narcotic or sedative of the brains afferent output to pump musclesuch as the diaphragm and chest wall, resulting in inadequate tidalvolume and associated fall in minute ventilation and a progressive risein carbon dioxide levels. The rise in carbon dioxide levels causesfurther suppression of the arousal response, therefore, potentiallycausing respiratory arrest. This first cause of respiratory arrestassociated with sedation or narcotics has been the primary focus ofprevious efforts to monitor patients postoperatively for the purpose ofminimization of respiratory arrests. Both oximetry and tidal CO2monitoring have been used to attempt to identify and prevent thisdevelopment. However, in the presence of oxygen administration, oximetryis a poor indicator of ventilation. In addition, patients may have acombined cause of ventilation failure induce by the presence of bothupper airway instability and decreased diaphragm output as will bediscussed, this complicates the output patterns of CO2 monitors makingrecognition of evolving respiratory failure due to hypoventilation moredifficult for conventional threshold alarm based systems.

The second factor causing respiratory arrest due to narcotics orsedatives relates to depression of the brains afferent output to upperairway dilator muscles causing a reduction in upper airway tone. Thisreduction in airway tone results in dynamic airway instability andprecipitates monomorphic cluster cycles of airway collapse and recoveryassociated with the arousal response as the patient engages in arecurrent and cyclic process of arousal based rescue from each airwaycollapse. If, despite the development of significant cluster of airwaycollapse, the narcotic administration or sedation is continued, this canlead to further prolongation of the apneas, progression to dangerouspolymorphic desaturation, and eventual respiratory arrest. There is,therefore, a dynamic interaction between suppression of respiratorydrive, which results in hypoventilation and suppression of respiratorydrive, which results in upper airway instability. At any given time, apatient may have a greater degree of upper airway instability or agreater degree of hypo ventilation. The relative combination of thesetwo events will determine the patterns of the output of the monitor.

Unfortunately, this has been one of the major limitations of carbondioxide monitoring. The patients with significant upper airwayobstruction are also the same patients who develop significant hypoventilation. The upper airway obstruction may result in drop out of thenasal carbon dioxide signal due to both the upper airway obstruction, onone hand, or due to conversion from nasal to oral breathing during arecovery from the upper airway obstruction, on the other hand. Althoughbreath by breath monitoring may show evidence of apnea, conversion fromnasal to oral breathing can reduce the ability of the CO2 monitor toidentify even severe hypoventilation in association with upper airwayobstruction, especially if the signal is averaged or sampled at a lowrate. For this reason, conventional tidal CO2 monitoring when appliedwith conventional monitors without out cluster pattern recognition maybe least effective when applied to patients at greatest risk, that is,those patients with combined upper airway instability andhypoventilation. The present inventors recognized that this uniquephysiologic process of reentry of airway collapse could be exploited toprovide a system and method for the recognition of the waveform patternsof airway instability. Several early embodiments are described in U.S.Pat. No. 6,223,064 (which is assigned to the present inventor, thedisclosure and the entire contents of which are incorporated byreference is if completely disclosed herein). These systems and methodsexploit the underlying cyclic physiologic process, which drives theperpetuation of a cluster of airway closures, to provide automaticrecognition and indication of upper airway instability in real time. Asdiscussed, the underlying cyclic process, which defines the behavior ofthe unstable upper airway, is associated with precipitous changes inventilation and attendant precipitous changes in monitored parameters,which reflect and/or are induced by such ventilation changes. Forexample, cycling episodes of airway collapse and recovery producessequential precipitous changes in waveform output defining analogouscluster waveforms in the time series of: oximetry derived pulse, airflowamplitude or/or tidal frequency, the oximetry S_(p)O₂, the chest wallimpedance and/or motion. EKG pulse rate, and/or R to R interval, EEG(due to clustering of arousals), EMG due to clustering of motor responseto arousals, systolic time intervals, and other parameters which varywith the brisk clustered cycles of apnea and recovery. EEG is readilyavailable in the hospital as BIS monitors, according to the presentinvention the detection of clusters of alpha or high amplitude, mixedfrequency arousals in clusters is very useful to indicate the potentialpresence of airway instability. According to the present invention,anyone of these parameters singularly or in combination can be used inthe hospital to detect either the absolute presence of airwayinstability or to provide evidence of probable airway instability sothat hospital personnel know that additional testing should be applied.

Conventionally, in the hospital, the analysis of one or more time seriesdatasets is widely used to characterize the behavior of physiologicsystems and to identify the occurrence adverse events. One basicconventional hospital montage commonly connected to a central monitor bytelemetry includes electrocardiogram (EKG), pulse oximetry, and chestwall impedance). Using this grouping of monitors, the human physiologicsystem produces a large array of highly interactive time series outputs,the dynamic relational configurations of which have substantialrelevance when monitored over both brief and long time intervals. Thepresent inventors recognized that multiple unique patterns of airwayinstability were present along the time series and that these differentpatterns could be identified to provide an interpretive output such atextual output and/or other alarm. In addition, the present inventorsrecognized that the complexity and time course variability of thesepatterns commonly overwhelms hospital workers so that timelyintervention is often not applied, resulting in unnecessary death orpatient injury. The inventors further recognized that the processedbased recognition of these patterns could be used to take action in theinterest of the health of the patient, such as automatically lock outnarcotic or sedation medication or increase the level and/or type ofventilation support. They also recognized that combined central andsatellite processing systems such as those used in the hospital basedsystems discussed supra, could be modified to provide such automaticrecognition and to provide such output and/or take such action toimprove the health care of patients such as automatically locking out adrug infusion’ upon the recognition of the interval development of anunstable pattern potentially indicative of an adverse drug reaction ortitration of continuous positive pressure devices. The invention alsoprovides a method of doing business to improve the sale of patientmonitoring systems, CPAP, and disposable probes for use with themonitors.

According one aspect of the present invention, the recognition ofsequential precipitous events or pathophysiologic patterns can beachieved by analyzing the spatial and/or temporal relationships betweenat least a portion of a waveform of a physiology parameter, (such as,for example, those listed supra), induced by at least a first episode ofairway collapse and at least a portion of a waveform induced by at leasta second episode of airway collapse. This can include the recognition ofa pattern indicative of a cluster, which can compromise a high count ofapneas with specified identifying features which occur within’ a shorttime interval along said waveform (such as 3 or more apneas within about5-10 minutes) and/or can include the identification of a waveformpattern defined by closely spaced episodes of airway collapse definingwaveform clusters. Further, the recognition can include theidentification of a spatial and/or temporal relationship defined bywaveform clusters, which are generated by closely spaced sequentialapneas due to cycling upper airway collapse and recovery.

According to another aspect of the invention, the patterns of thesecomplex interactive signals and the data sets defining path physiologicupper airway instability are characterized by organizing the time seriesinto an ascending hierarchy of objects (which in one preferredembodiment are substantially in the time domain), ordering these objectsinto a relational data matrix and then recognizing the complexreciprocations across time series and across scales and by applying anexpert system to that set of highly organized set of objects.

For the purpose of organizing and identifying physiologic datasets,according to the present invention a fundamental dynamic time seriesobject is identified and characterized, which possesses a uniquesymmetry of scale. The inventors call this object a “physiologicreciprocation”. For the purpose of pattern recognition, according to thepresent invention, a physiologic reciprocation is a fundamentalvariation time series output generated by an organ, an organ system,and/or an entire organism, which is at least partially reversed within aspecified interval. According to the present invention reciprocations,as recognized by the processor, are widely scalable across substantiallyall fundamental output patterns of organ function and physiologiccontrol. The present inventors recognized that an scaleable system whichrecognized and analyzed reciprocations along a time series, acrossdifferent scales of the time series, and between different scales ofdifferent contemporaneously derived time series, could be used toreadily identify specific dynamic physiologic patterns of interactiondefining both different states of disease and health. Further, thepresent inventors recognized that, for the purpose of processor basedpattern recognition, human physiologic function (and dysfunction) can becharacterized by defining and recognizing a object hierarchy ofphysiologic reciprocations ordered into an ascending, inheritance basedrelational timed data matrix.

Using the above discoveries the present inventors recognized thattypical standard central hospital monitors including those with wirelesscapabilities (such as the system described for example U.S. Pat. No.6,364,834) and outpatient holter type monitors can be improved toprovide automatic recognition of airway instability and sleep apnea andto provide an automatic visual or audible indication of the presence ofsuch clusters and further to provide a visual or audible output andseverity of this disorder thereby rendering the timely recognition anddiagnosis of upper airway instability and obstructive sleep apnea asroutine and automatic in the hospital as the diagnosis of other commondiseases such as hypertension.

FIG. 3 a illustrates the reentry process driving the propagation ofairway instability reentry clusters. The physiologic basis for theseclusters has been previously described in U.S. Pat. Nos. 5,891,023 and6,223,064 and provisional application 60/291,691 (the entire contents ofeach of which are incorporated by reference as if completely disclosedherein). This cycle is present when the airway is unstable but thepatient is capable of arousal. In this situation, in the sleeping orsedated patient, upon collapse of the airway, the patient does notsimply die, she rescues herself and precipitously opens the airway torecover by hypoventilation. however, if the airway instability remainsafter the arousal and rescue is over, the airway collapses again, onlyto be rescued again thereby producing a cluster of closely spaced apneaswith distinct spatial, frequency and temporal waveform relationshipsbetween and within apneas wherein the physiologic process reenters againand again to produce a clustered output. According to the presentinvention, an airway instability cluster is comprised of a plurality(two or more) of closely spaced apneas or hypopneas but the use of 3 ormore apneas is preferred. The present invention includes (but is notlimited to) recognition of airway instability clusters in oxygensaturation, pulse, chest wall impedance, blood pressure, airflow(including but not limited to exhaled carbon dioxide and airtemperature). nasal and oral pressure, systolic time intervals,electrocardiograph tracings (including pulse rate and R to R intervalplots), timed plots of ST segment position, chest wall and/or abdominalmovements (as by strain gauge, impendence, or other methods),electromyography (EMG), and electroencephalography (EEO). For all ofthese waveforms the basic underlying cluster pattern is similar and thesame basic presently preferred cluster pattern recognition system andmethod, according to the present invention, can be applied to recognizethem.

According to one aspect of the invention, a microprocessor system isprovided for the recognition of specific dynamic patterns of interactionbetween a plurality of corresponding and related time series, the systemcomprises a processor programmed to; process a first time series toproduce a lower-level time series of sequential time series fragmentsderived from the first time series, process the lower-level time seriesto produce a higher-level time series comprised of sequential timeseries fragments from the lower-level time series, process a second timeseries, the second time series being related to the first time series,produce a second lower-level time series of sequential time seriesfragments derived from the second time series, and identify a dynamicpattern of interaction between the first time series and the second timeseries. The system can be further programmed to process the lower-leveltime series of the second time series to; produce a higher-level timeseries derived from sequential time series fragments of the secondlower-level, time series. The system can be programmed to process athird time-series, the third time series being related to at least oneof the first and the second time series, to produce a third lower-leveltime series of sequential time series fragments derived from said thirdtime series. The system can be programmed to process the higher-leveltime series to produce a complex-level time series derived fromsequential time series fragments of said higher-level time series. Thetime series fragments of the first and second time series can be storedin a relational database, the fragments of the higher-level time seriescan comprise objects, the objects inheriting the characteristics of theobjects of the lower-level time series from which they are derived. Thefirst and second time series can comprise datasets of physiologic datapoints and the system can comprise a patient monitoring system whereinthe dynamic pattern of interaction comprises convergent clusters ofpathologic reciprocations.

It is the purpose of the present invention to provide a diagnosticsystem, which can convert conventional hospital-based central telemetryand bard wired monitoring systems and portable home systems to provideprocessor based recognition of airway instability through therecognition of patterns of closely spaced reciprocations and/or eventsinduced by apneas and/or hypopneas both in real time and in overnightinterpretive format and which can automatically lock-out narcoticinfusion upon recognition of patterns of instability.

It is the purpose of the present invention to provide a system, whichidentifies, maps, and links waveform clusters of airway instability fromsimultaneously derived timed signals of multiple parameters includingchest wall movement, pulse, airflow, exhaled carbon dioxide, systolictime intervals, oxygen saturation, EKG-ST segment level, EEG, EMG, andother parameters to enhance the real-time and overnight diagnosis ofairway instability.

It is further the purpose of the present invention to provide a systemto provide a graded index and/or indication of patterns of airwayinstability.

It is further the purpose of the present invention to provide a system,which provides characterization of different types of patterns ofventilation and/or upper airway instability.

It is further the purpose of the present invention to provide a system,which provides characterization and/or differentiation of differenttypes of patterns such as monomorphic, polymorphic, and combinedpatterns of instability.

It is further the purpose of the present invention to provide a system,which provides characterization to output an indication of the type ofpattern identified by the processor so that a decision relevant theprobability of success of auto titration with CPAP and/or BIPAP can bemade.

It is further the purpose of the present invention to provide timely,real-time indication such as a warning or alarm of the presence ofairway instability clusters so that nurses can be aware of the presenceof a potentially dangerous instability of the upper airway duringtitration of sedatives and/or narcotics.

It is further the purpose of the present invention to provide a systemfor the recognition of airway instability for combined cluster mappingof a timed dataset of nasal oral pressure with tidal CO2 to identifyclusters of conversion from nasal to oral breathing and to optimallyrecognize clusters indicative of airway instability in association withtidal CO2 measurement indicative of hypoventilation.

It is the purpose of the present invention to provide an iterativeobject oriented waveform processing system, which can characterize,organize, and compare multiple signal levels across a plurality ofsignals by dividing each waveform level of each signal into objects fordiscretionary comparison within a relational database, object databaseor object relational database.

It is further the purpose of the present invention to provide a system,which automatically triggers lockout of medication infusion based on therecognition of an adverse pattern of instability along at least onetimed dataset output.

It is another, aspect of the present invention to provide a system thatautomatically customizes treatment algorithms or diagnostic algorithmsbased on the analysis of waveforms of the monitored parameters.

It is further the purpose of the present invention to provide a system,which provides recognition and characterization of physiologicreciprocations across different time series scales.

It is further the purpose of the present invention to provide a system,which automatically triggers testing (and comparison of the output) of asecondary intermittently testing monitor upon the recognition ofpatterns indicative of physiologic instability.

It is further the purpose of the present invention to provide real timeprotection to patients against adverse drug and to provide a data matrixcomprising matched time series of physiologic signals with a time seriesof drug infusion so that hospital personnel can readily match specificpatterns of pathophysiologic perturbations to specific types ofmedications and ranges of medication dosage for patients hospital wide.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic of a hospital central processing system foroutputting and/or taking action based on the analysis of the time seriesprocessing according to the present invention.

FIG. 2 shows the organization of airflow waveforms into ascending objectlevels identifying pathophysiologic reciprocations, which demonstraterecognizable symmetry of scale according to the present invention. Notethat, according to the present invention, reciprocations at the complexand composite levels inherit the reciprocations from the levels belowthem in ascending order.

FIG. 3 a shows an illustration of the complexity of the mechanismsdefining the timed interactions of physiologic systems induced by upperairway instability, which the present inventor calls an “airwayinstability cluster reentry cycle” showing the derivativereciprocations, which are generated by the cycling producing thecorresponding cluster of reciprocations.

FIG. 3 b shows a schematic object mapping at the composite level of twosimultaneously measured parameters, which are perturbed to produceclusters in response to the upper airway instability cycles of FIG. 3 a,which is automatically detected according to the present invention.

FIG. 3 c shows an example of a monomorphic cluster pattern indicative ofairway instability and derived from the mechanism of FIG. 3 a, which isautomatically detected according to the present invention. The figureshows a raw data set of pulse rate (by oximetry), airflow (by nasal-oralthermister).

FIG. 3 d shows another example of monomorphic cluster pattern derivedfrom the mechanism of FIG. 3 a, which now includes a corresponding pulserate cluster, which is automatically detected according to the presentinvention. Note the fundamental airflow positive and negativereciprocations in this figure generate larger scale positivereciprocations of airflow recovery (with the patterns shown in FIG. 3 b)

FIG. 3 e shows a schematic representation of a portion of a multi-signalobject as derived from the multiple corresponding time series of FIG. 3c with three multi-signal recovery objects at the composite object levelidentified for additional processing according to the present invention.Note that objects at the composite level encapsulate the waveformobjects from lower levels.

FIG. 4 show a shows a three-dimensional representation of thecylindrical data matrix comprised of corresponding, streaming, timeseries of objects from four different timed data sets, with each of thefour data sets divided into an ascending hierarchy of 3 levels.

FIG. 5 shows a selected subordinate composite object of oxygensaturation of FIG. 3 c, matched with its corresponding primary compositeobject of airflow, as they are stored as objects at the composite levelin the relational database, object database or object relationaldatabase.

FIG. 6 shows a comparison between two data sets of tidal airflowreciprocations at the fundamental level wherein the second data setshows evidence of expiratory airflow delay during the recovery object,note that the recovery object is recognized at the composite level andit has inherited (and therefore encapsulates) the fundamental tidalairflow reciprocation objects.

FIG. 7 shows a schematic object mapping at the composite level of twosimultaneously measured parameters with a region of anticipatedcomposite objects according to the present invention.

FIG. 8 shows a schematic object mapping and scoring at the compositelevel of two simultaneously measured parameters with the region ofanticipated composite objects according to the present invention.

FIG. 9 shows a schematic of a system for customizing a CPAPauto-titration algorithm based on the analysis of reciprocations ofmultiple corresponding signals across multiple scales.

FIG. 10 a shows a typical monomorphic airway instability cluster alongtimes series of oximetry, airflow, and thorax movement.

FIG. 10 b shows a polymorphic airway instability cluster along timesseries of oximetry, airflow, and thorax movement.

FIG. 10 c shows a times series of oximetry over a period of about 2hours and 40 minutes demonstrating a monomorphic airway instabilitycluster of negative oxygen saturation reciprocations degenerating into apolymorphic cluster exhibiting marked instability of control evidencedby marked reciprocations of the peaks and nadirs of the negativeoximetry reciprocations within the cluster. This represents a markedlyadverse pathophysiologic pattern, which is automatically recognized andgraded according to the present invention.

FIG. 10 d shows a 16.2-minute segment of the times series of oximetry ofFIG. 10 d illustrating a modest decline component of a pathophysiologicpattern of monomorphic reciprocation.

FIG. 10 e shows a 12.S-minute segment of a times series of oximetryillustrating a more severe decline of the peaks comprising degenerationinto a polymorphic pattern indicative of severely adversepathophysiologic pattern and which can be indicative of an importantdecline in arousal threshold and incompletes arousal response whichfails to completely correct the negative reciprocation. This polymorphicpattern is automatically recognized according to the present inventionto provide an output indication or to take action such as to lock outmedication.

FIG. 10 e shows a 19.2-minute segment of a time series of oximetryillustrating a modest variation of the peaks of reciprocations withnegative reciprocations of the nadirs each said reciprocation becomingmore negative. This peak variation is consistent with a mild polymorphiccluster. Note that the variations of the nadirs can be indicative of animportant instability and decline in arousal threshold despite acomplete or nearly complete arousal response. This pattern isautomatically recognized according to the present invention to providean output indication or to take action such as to lock out medication.

FIG. 11 show a diagram represents both the real-world objects (e.g.reciprocations) and support objects for the rapid creation, rendering ofsuch object to a graphical user interface and persisting such objects torelational database and/or a hierarchical data store (e.g. eXtensibleMarkup Language (XML) documents).

FIG. 12 shows core objects of the presently preferred analysis.

FIG. 13 shows the preferred support mechanisms for the creation of theanalysis.

FIG. 14 shows one preferred data acquisition and transformation systemfor preceding the analysis.

FIG. 17 shows one presently preferred nomenclature for the exemplaryparameters of airflow, pulse, and oxygen saturation. Illustrativeexamples of designation by this nomenclature are shown in FIG. 18.

FIG. 19 shows a schematic of a system for automatically changing theprocessing analysis of subsequent time-series portion based on theanalysis output of an earlier portion of the time series.

DESCRIPTION OF THE PRESENTLY PREFERRED EMBODIMENTS

One presently preferred system for processing, analyzing and acting-on atime series of multi-signal objects is shown in FIG. 1. The examplesprovided herein show the application of this system for real timedetection, monitoring, and treatment of upper airway and ventilationinstability although the present invention is useful for detecting abroad range of patterns and instabilities (as described in co pending.The system includes a portable bedside processor 5 preferably having atleast a first sensor 20 and a second sensor 25, which preferably provideinput for at least two of the signals discussed supra. The systemincludes a transmitter 35 to a central processing unit 37. The bedsideprocessor 5 preferably includes an output screen 38, which provides thenurse with a bedside indication of the sensor output. The bedsideprocessors can be connected to a controller of a treatment orstimulation device 50, which can include a drug delivery system such asa syringe pump, a positive pressure ventilation device, an automaticdefibrillator, a tactile stimulator, the processor itself, to adjust theanalysis of the time-series inputs. The central unit 37 preferablyincludes as output screen 55 and printer 60 for generating a hard copyfor physician interpretation. According to present invention, as will bediscussed in detail, the system thereby allows recognition of airwayinstability, complications related to such instability, andpathophysiologic divergence in real time from a single or multipleinputs. The bedside processor is preferably connected to a secondaryprocessor 40 which can be a unit, which takes action such as locking outa medication infusion, performing measurements intermittently and/or ondemand such as a non-invasive blood pressure monitor or an ex-vivomonitor, which draws blood into contact with a sensor on demand fortesting to derive data points for addition to the multi-signal objects.The secondary processor 40 includes at least one sensor or controller45. The output of the bedside processor can either be transmitted to thecentral processor 37 or to the bedside monitor 5 to render a new objectoutput, action, or analysis.

In one example the occurrence of a dynamic clustering of apneas can beidentified and the infusion pump (which can be for example a patientcontrolled analgesia (PCA) device can be automatically locked out toprevent further infusion and ad an output such as “Caution—patternsuggestive of mild upper airway instability at dose of 1 mg Morphine.”If in this example the nurse increases the dose to 2 milligram and thepattern shows an increase in severity an output such as “Patternsuggestive of moderated upper airway instability at dose of 2 mg/hr. ofMorphine-morphine locked out”. To maintain Morphine dose at the 2 mg.level for this patient, the nurse or physician would have to overridethe lockout and preferably document the reason for override. Upon anoverride the processor then tracks the severity of the clusters and ifthe clusters reaches an additional severity threshold an output such as“Severe upper airway instability—Morphine locked out” is provided.According to the present invention the onset of the drug administrationis recognized by the central processor and the dose administered becomesa time series, which can then be matched to the patterns of themonitored physiologic time series. In addition to real time protection,the data matrix comprising matched time series of physiologic signalswith a time series of drug infusion provides an ability for hospitalpersonnel to readily match specific patterns of pathophysiologicperturbations to specific types of medications and ranges of medicationdosage for patients hospital wide.

FIG. 2 illustrates the ascending object processing levels according tothe present invention, which are next applied to order the objects torecognize the patterns which are identified by the system of FIG. 1 toprovide an automatic indication and/or action based a pattern indicativeof an adverse physiologic occurrence. In the preferred embodiment, theselevels are defined for each signal and comparisons can be made acrossdifferent levels between different signals. Physiologic reciprocationsare identified (if present) and characterized at each level. In FIG. 2the first level is comprised of the raw data set. The data from thisfirst level are then converted by the processor into a sequence offundamental segments called dipoles to form the second fundamentalobject level. In one embodiment, all of the objects, which willultimately define complex multi-signal objects, are comprised of thesesequential fundamental objects having the simple characteristics ofslope, polarity, and duration. At this level, the dipoles are used torender the next level, called the “composite object level”.

The composite object level is comprised of sequential and overlappingcomposite objects, and particularly reciprocations, which are composed,of a specific sequence of dipoles as defined by selected criteria. Eachof these composite objects has similar primary characteristics of aslope, duration, and polarity to the fundamental objects. However, forthe composite objects, the characteristic of slope can comprise a timeseries characteristic given as a slope dataset. The composite objectlevel also has the characteristic of “intervening interval time-series”defined by a time series of the intervals between the recognized orselected composite objects. At this level a wide range of discretionaryindex characteristics can be derived from the comparison of basiccharacteristics of composite objects. Examples of such indexcharacteristics include; a “shape characteristic” as derived from anyspecified portion of the slope dataset of the object, a “positionalcharacteristic” as derived from, for example, the value of the lowest orhighest points of the object, or a “dimensional value characteristic” asderived by calculating the absolute difference between specified datapoints such as the value of the lowest and the highest values of theobject, or a “frequency characteristic” such as may be derived fromperforming a Fourier transform on the dataset of the object.

The next analysis level is called the “complex object level”. In thislevel, each sequential complex object comprises plurality of compositeobjects meeting specific criteria. According to the present invention,the patterns of reciprocations at the complex level are representativeof a balance between physiologic perturbation and the control. As suchthey are useful to characterize the integrity of control of the organismas well as the severity and character of the perturbation. According tothe present invention, complex physiologic reciprocations are generallyderived from composite level reciprocations and inherit theircharacteristics. Therefore a complex object (such as a recovery object)inherits the composite (tidal breath) reciprocations from which it isderived and this is exploited by the system as will be discussed.Complex objects have the same categories of primary characteristics andderived index characteristics as a composite object. A complex objectalso has the additional characteristics of “composite object frequency”or “composite object order” which can be used as search criteria as partof an overlaid expert system. These can be defined by a selectedfrequency or order of composite object types, which are specified asdefining a given complex object. A complex object also has additionalhigher-level characteristics defined by the time-series of the shapes,dimensional values, and positional characteristics of its componentcomposite objects. As described for the composite objects, similar indexcharacteristics of the complex objects can be derived from thesecharacteristics for example; a complex object can be derived as acluster of composite level reciprocations.

In a further example a complex level reciprocation given by a timeseries of positional characteristic along a clustered grouping ofsequential composite objects (such a reciprocation object derived ofsequential nadir points or characteristics of composite levelreciprocations of a cluster object at the complex level) can be readilyidentified and characterized. FIG. 10 f shows a superimposed cluster ofnegative reciprocations of the reciprocation nadirs. Initial fallportions of a severe negative reciprocations of the nadirs of airwayinstability clusters are shown in FIGS. 10 d and 10 e and in onepresently preferred embodiment these are automatically recognized andindexed relevant magnitude and slope of the decline to provide an outputor take action as previous discussed. The present inventors recognizedthat such a patterns can indicate a high degree of instability of thearousal threshold in relation to a given level of oxygen saturation.This may indicate that other factors are dominating relevant the arousalthreshold (such as CO2). However, this negative reciprocation (likevirtually all physiologic reciprocations) is indicative of the functionand integrity of a control system. As such a severely negativereciprocation of the nadirs (or the initial decline component as shown,in FIGS. 10 c and 10 d) can indicate a dangerous attenuation of thecontrol system defining the arousal threshold (as due to narcotics,sedation, a change in sleep stage, or simply a generally less stablearousal control system). In the alternative, a reciprocation of the peakcharacteristics of such clusters may be identified (or again the initialdecline component as shown in FIGS. 10 c and 10 d). The inventorsrecognized that this pattern can be indicative of an inadequate and/orattenuated arousal response so that the clustered reciprocations whichexhibit the falling peaks are not incomplete. The identification of oneor more severe negative reciprocations of the peaks or the declinecomponent alone of such reciprocations can provide important evidence ofinadequate arousal response as can be induced by narcotics and/or asuperimposed hypo ventilation disorder. Such patterns inside patterns(i.e. objects inside objects) are readily recognized according to thepresent invention along a single level and across levels, and areautomatically recognized and outputted for indication such as a gradedalarm or for the automatic taking of action.

Characteristics or index characteristics may be combined with others.For example, a shape characteristic (which contains the slope data setin the time domain) may be combined with a frequency characteristic toprovide a time series of a mathematical index of the slopes and thefrequencies of the composite objects which can be ordered in an objecthierarchy of ascending object data sets and correlated with other objectlevel of other object data sets.

The next level, termed the “global objects level” is then derived fromthe time series of complex objects. At this level global characteristicsare derived from the time series datasets of complex objects (and all oftheir characteristics). At the global objects level, the processor canidentify general specific patterns over many hours of time. An exampleof one specific pattern which is readily recognizable at this levelwould be a pattern of regular monotonous occurrence of negativereciprocations alternating with positive reciprocations at the complexlevel comprised of composite objects, wherein the composite objects arederived of regular reciprocations of negative reciprocations alternatingwith positive reciprocations (of tidal breathing) at the composite leveland wherein the magnitude of both the negative and positivereciprocations at the composite level oscillates in a regular frequency.This pattern is typical of Cheyenne-Stokes Respirations and according tothe present invention an expert system examines the pattern of theobjects at the composite level, the complex level, and finally at theglobal level to provide distinction between this process and thepatterns of upper airway instability.

Additional higher levels can be provided if desired as by a“comprehensive objects level” (not shown), which can, for example,include multiple overnight, or post operative monitoring periods whereina comprehensive object is comprised of a dataset of “global objects”.

While FIG. 2 illustrates the levels of object derivations of aventilation signal, in another example a similar hierarchicalarchitecture can be derived for the timed data set of the pulse waveform(as from an arterial pressure monitor or the plethesmographic pulse) orany of the parameters discussed below. For the plethesmographic pulsethe fundamental level is provided by the pulse tracing itself andincludes all the characteristics such as ascending and descending slope,amplitude, frequency, etc. This signal also includes the characteristicof pulse area (which, if applied to a precise signal such as the flowplot through the descending aorta, is analogous to tidal volume in thefundamental minute ventilation plot). When the pulse signal isplethesmographic, it is analogous to a less precise signal ofventilation such as nasal pressure or thermister derived airflow. Withthese less precise measurements, because the absolute values are notreliable indicators of cardiac output or minute ventilation, the complexspatial relationships along and between signals become more importantthan any absolute value of components of the signal (such as absoluteamplitude of the ascending pulse or inspiration curve). In other word,the mathematical processing of multiple signals that are simply relatedto physiologic parameters (but are not a true measurement of thoseparameters) is best achieved by analyzing the complex spatialrelationships along and between those signals. To achieve this purpose,according to the present invention, as with ventilation, the pulsesignal is organized into a similar multi-level hierarchy of overlappingtime series of objects. Subsequently these are combined and comparedwith the processed objects of respiration. The relationships between aplurality of streaming data sets of the ascending objects of FIG. 2 areconceptually represented in FIG. 4 (discussed below) which can include ahierarchy of lime series of streaming objects indicative of a timed druginfusion. A time series of such infusion can include, for example,reciprocations derived from bolus infusions superimposed on a baseline(as with a patient controlled analgesia pump). Here, as discussed, allthe time series (included those of the drug infusion) can be orderedinto ascending hierarchy and correlated with each of the differentlevels of other time series.

FIG. 3 a shows an exemplary pathophysiologic process associated with acharacteristic dynamic pattern of interaction. As discussed previously,this cyclic process is induced by upper airway instability producingderivative reciprocations across a wide range of physiologic parameters.FIG. 3 b shows a schematic object mapping at the composite level of twosimultaneously measured parameters which are perturbed to produceclusters in response to the upper airway instability cycles of FIG. 3 a,this schematic demonstrates the basic cluster pattern of timed plots,the schematic shows the airway instability clusters as negativereciprocations but according to the present invention they can also berecognized as positive oscillations these can also be recognized aspositive reciprocations. According to the present invention, upperairway instability and/or ventilation instability is detected byrecognizing these cluster patterns along one or more of the timed plotsof: EEG frequency and/or amplitude, chest and/or abdomen movement (byimpedance, strain gauge or other method), pulse rate and or RR interval(by oximetry, and/or EKG or other method), pulse amplitude and/or pulsetransit time, EMG, oxygen saturation (by pulse oximetry, and/orintravascular venous or arterial oximetry), continuous blood pressure,exhaled carbon dioxide, nasal flow and/or pressure (as by thermisterand/or pressure monitoring, or other method), minute ventilationmeasurements (as by pneumotachometer or other method), snoring (as bypressure monitoring and/or microphone or other method), airway impedance(as by oscillation or other method).

FIG. 3 c, and 10 a show examples of a monomorphic cluster patternindicative of airway instability and derived from the mechanism of FIG.3 a, the patterns of which are automatically detected according to thepresent invention. FIG. 3 c shows a raw data set of pulse rate (byoximetry), airflow (by nasal-oral thermister). Another example is shownin FIG. 3 d, which now includes a corresponding pulse rate cluster,which is automatically detected according to the present invention. Notethe fundamental airflow positive and negative reciprocations in thisfigure generate larger scale positive reciprocations of airflow recovery(with the patterns shown in FIG. 3 c).

In the presently preferred embodiment, the time series of each of thesesignals are processed into time domain fragments (as objects) andorganized into the object levels as previously discussed. For thepurpose of organizing and analyzing complex interactions between thesecorresponding and/or simultaneously derived signals, the same basicascending process is applied to each signal. As shown in FIG. 3 e thesestreaming objects, many of which overlap, can be seen to conceptuallyproject along a three dimensional time series comprised of multiplelevels of a plurality of corresponding signals. A “multi-signal object”is comprised of at least one object from a first signal and at least oneobject from another signal.

This type of representation is too complex for presentation to hospitalpersonnel but is preferred for the purpose of general representation ofthe data organization because, at this level of complexity, a completerepresentation of multiple time series does not lend itself well to atwo-dimensional graphical (and in some cases a three dimensional)representation.

To illustrate the complexity ordered by this approach, consider thecomponents of just one of the three simple recovery objects shown inFIGS. 3 c and 3 e. This single recovery object includes the followingexemplary characteristics, each of which may have clinical relevancewhen considered in relation to the timing and characteristics of otherobjects;

-   -   1. Amplitude, slope, and shape of the oxygen saturation rise        event at the composite level.    -   2. Amplitude, slope, and shape of the ventilation rise event at        the composite Level which contains the following characteristics        at the fundamental level;        -   Amplitude, slope, time and shape of the inspiration rise            object        -   Amplitude, slope, time and shape of the expiration fall            object.        -   Frequency and slope dataset of the breath to breath interval            of reciprocations (tidal breathing) objects    -   3. Amplitude, slope, and shape of the pulse rise event at the        composite level which contains the following exemplary        characteristics at the fundamental level;        -   Amplitude, slope, and shape of the plethesmographic pulse            rise event.        -   Amplitude, slope, and shape of the plethesmographic pulse            fall event.        -   Frequency and slope datasets of beat-to-beat interval of the            pulse rate.

As is readily apparent, it is not possible for a health care worker totimely evaluate the values or relationships of even a modest fraction ofthese parameters and an expert system applied generally to theseparameters rapidly becomes inordinately complex and cumbersome. For thisreason the output based on the analysis of these time series of objectsare optimally first ordered into a relational, inheritance based objecthierarchy, and then subjected to expert system analysis and/or presentedin a succinct and interpretive format to the hospital worker as will bediscussed.

FIG. 4 provides a conceptual representation of one presently preferredrelational data structure of multiple time series, according to thepresent invention. This representation shows that the many time seriesof objects are organized into different corresponding streams ofobjects, which can be conceptually represented as a cylindrical matrix1, with time defining the axis along the length of the cylinder 1. Inthis example the cylinder 1 is comprised of the four streams of objectseach stream having three levels and these are matched and storedtogether in a relational database, object database or object-relationaldatabase. Each streaming set of objects from a single signal or source(e.g. airflow or oximetry, as in a matrix of physiologic signals) isrepresented in the main cylinder 1 by a smaller cylinder (2,3,4,5) andeach of these smaller cylinders is comprised of a grouping of ascendingobject levels (6,7) as will be described. One important advantage oforganizing the data in this way is that each object from each groupingcan be readily compared and matched to other objects along the groupingand can further be compared and matched to other object from each othergrouping. Objects occurring at one lime in one level encapsulate theobjects at lower levels so that objects of a first grouping can bereadily compared to objects occurring at another time and at anotherlevel of at least one other grouping. Complex patterns and subtlerelationships between interactive and interdependent streams of objectscan be readily defined by applying an expert system or by manually orautomatically searching the matched object streams as will be discussed.This allows for the recognition of the “Dynamic Pattern of Interaction”(DPI) between data set objects. The recognition of a specific DPIoccurrence falling within a specified range is used to determine thepresence and severity of a specific of a biologic or physical process.

One of the longstanding problems associated with the comparison ofoutputs of multiple sensors to derive simultaneous multiple time seriesoutputs for the detection of pathophysiologic change is that theaccuracy and/or output of each sensor may be affected by differentphysiologic mechanisms in different ways. Because of this, the value ofmatching an absolute value of one measurement to an absolute value ofanother measurement is degraded. This is particularly true if themeasurement technique or either of the values is imprecise. For example,when minute ventilation is measured by a precise method such as apneumotachometer, then the relationship between the absolute values ofthe minute ventilation and the oxygen saturation are particularlyrelevant. However, if minute ventilation is being trended as by nasalthermister or nasal pressure monitoring or by chest wall impedance, thenthe absolute values become much less useful. However, according to oneaspect of the present invention the relationship between pluralities ofsimultaneously derived signals can be determined independent of therelationships of the absolute values of the signals. In this way,simultaneously derived signals can be identified as having convergenceconsistent with physiologic subordination or divergent shapes consistentwith the development of a pathologic relationship or inaccurate dataacquisition.

As noted, with physiologically linked signals, a specific occurrence ormagnitude of change in one signal in relationship to such a change inanother signal may be more important and much more reproducible than theabsolute value relationships of the respective signals. Using theteachings of the present invention, two simultaneously acquired andphysiologically linked signals are compared by the microprocessor overcorresponding intervals by matching the respective objects (which can beapplied at multiple levels) between the signals. As discussed, a primarysignal such as airflow is ordered into composite and complex objectsalong with a contemporaneously measured secondary signal such as oxygensaturation as by the method and system discussed previously. Forexample, the composite level of airflow can be a of reciprocationobjects derived from a fundamental level of amplitude and/or frequencyof the tidal airflow as by thermister or pressure sensor, or anotherplot, which is indicative of the general magnitude of the timed tidalairflow. In the presently preferred embodiment, a plot at thefundamental level of a mathematical index (such as the product) of thefrequency and amplitude is preferred, because such an index takes intoaccount the important attenuation of both amplitude and frequency duringobstructive breathing. Furthermore, both the frequency and amplitude areoften markedly increased during the recovery interval between apneas andhypopneas.

Although the exact delay between the signals may not be known, theprocessor can identify this by identifying the best match between theobject sets. In the preferred embodiment, this “best match” isconstrained by preset limits. For example, with respect to ventilationand oximetry, a preset limit could be provided in the range of 10-40seconds although other limits could be used depending on the hardware,probe site and averaging intervals chosen. After the best match isidentified, the relationships between the signals are compared (forexample, the processor can compare the a rise or fall event of oxygensaturation to a rise or fall event of ventilation as shown in FIG. 6).In this preferred embodiment, each such event is compared. It isconsidered preferable that the objects of each respective parameterrelate to a similar duration. With respect to airflow, calculation ofthe magnitude value of airflow may require sampling at a frequency of 25hertz or higher, however, the sampling frequency of the secondary plotof the magnitude value of the index can, for example, be averaged in arange of one hertz to match the averaging interval of the data set ofoxygen saturation.

It is not necessary that such a fundamental level plot reflect exactlythe true value of the minute ventilation but rather, it is importantthat the plot reflect the degree of change of a given level of minuteventilation. Since these two signals are physiologically linked, anabrupt change in the primary signal (airflow) generally will producereadily identifiable change in the subordinate signal (oxygensaturation). As previously noted, since the events which are associatedwith airway collapse are precipitous, the onset of these precipitousevents represent a brief period of rapid change which allows for optimaldetection of the linkage between the primary signal and the subordinatesignal.

The signals can be time matched by dipole slopes at the fundamentallevel. In addition, in one preferred embodiment, the point of onset ofprecipitous change is identified at the composite object level of theprimary signal and this is linked to a corresponding point of aprecipitous change in the composite object level of the subordinatesignal. This is referred to herein as a delta point. As shown in FIGS. 3c, 5, and 6, a first delta point is identified in the primary signal andin this example is defined by the onset of a rise object. Acorresponding first delta point is identified in the subordinate signaland this corresponds to the onset of a rise object in the subordinatesignal A second delta point is identified which is defined by the pointof onset of a fall object in the primary signal and which corresponds toa second delta point in the subordinate signal defined by the onset of afall event in the secondary signal. The point preceding the second deltapoint (the “hyperventilation reference point”) is considered a referenceindicating an output associated with a degree of ventilation, whichsubstantially exceeds normal ventilation and normally is at least twicenormal ventilation. When applying airflow as the primary signal andoximetry as the subordinate signal, the first delta point match is themost precise point match along the two integrated waveforms andtherefore comprises a (“timing reference point”) for optimally adjustingfor any delay between the corresponding objects of the two or moresignals. The mathematical aggregate (such as the mean) of an index ofthe duration and slope, and/or frequencies of composite rise and fallobjects of the fundamental level of tidal ventilation along a shortregion adjacent these reference points can be applied as a generalreference for comparison to define the presence of relative levels ofventilation within objects along other portions of the airflow timeseries. Important fundamental object characteristics at these referencepoints are the slope and duration of the rise object or fail objectbecause these are related to volume of air, which was moved during thetidal breath. The fundamental objects comprising the tidal breaths atthe reference hyperventilation point along the composite level areexpected to have a high slope (absolute value) and a high frequency. Inthis way both high and low reference ranges are determined for thesignal. In another preferred embodiment, these points can be used toidentify the spatial shape configuration of the rise and fall objects atthe fundamental level during the rise and fall objects at the compositelevel.

Using one presently preferred method, a first object (such as is shownin FIG. 5) can then identified in the primary signal (such as, forexample, airflow) at the composite object level between the first deltapoint and the second delta point which is designated a recovery object.As also shown in FIG. 5 the matched recovery object (for example oxygensaturation) is also identified in the subordinate signal as the point ofonset of the rise object to the point of the onset of the nextsubsequent fall object. In the preferred embodiment, the recovery objectis preceded by the apnea/hypopnea object which is defined by the pointof onset of the fall object to the point of onset of the next riseobject in both the primary and subordinate signals.

Once the signals have been sufficiently matched at the one object levelthey can be further matched at another object level. When the objectsare matched, the baseline dynamic range relationship between the signalscan be determined. This baseline range relationship can be a magnitudevalue relationship or a slope relationship. The signals can then bemonitored for variance from this baseline range, which can indicatepathology or signal inaccuracy. The variance from baseline can be, forexample, an increase in the relative value of ventilation in relation tothe oximetry value or a greater rate of fall in oxygen saturation inrelation to the duration and/or slope of fall of ventilation. In anotherexample, the variance can include a change from the baseline delaybetween delta points along the signals or a change in the direction(polarity) of one signal in relation to the baseline relationship forexample two signals which formally moved in the same direction (afteradjusting for a delay) may be recognized to exhibit variance by movingin opposite directions and the occurrence of this variance from apreviously identified dynamic relationship (or a pre specified dynamicrelationship range) Upon such recognition, according to the presentinventor, the processor can be programmed to take action such as lockout a medication, adjust the flow of oxygen, change the positivepressure or tidal volume of a ventilator, or provide an indication, suchas an alarm.

According to the present invention, clusters of signal perturbationinduced by even mild hypopneas can generally be reliably recognized bytheir cluster patterns utilizing with only a single parameter withoutsetting up a priori and arbitrary rules (such as a 50% reduction in theairflow signal). In addition, when significant signal noise or reducedgain is present reducing cluster recognition in one signal, the objectsbased system can combine matched clusters within a time series ofmulti-signal objects in the presence of sub optimal signals by providinga scoring system for sequential objects across a wide range ofparameters. FIGS. 7, and 8 show schematics of the basic cluster matchingfor two parameters in situations wherein sub optimal signals may bepresent although many more parameters may be combined in this way. Inone example, the matched clusters can be paired timed datasets ofairflow and oximetry include a matched sequence of negativereciprocations in the airflow signal and corresponding delayed negativereciprocation in the oximetry signal. One exemplary method of achievingmatch with an incomplete set of matching objects in one of the signalsfollows; Each reciprocation at the composite level is defined by a setof coupled rise and fall objects meeting criteria and occurring within apredetermined interval of each other (as discussed previously). Theoccurrence of reciprocation in either dataset meeting all criteria isgiven a score of 1.

The reciprocations are counted in sequence for each matched clusterobject. For the purpose of illustration, according to the presentinvention, the occurrence of a score of 3 in anyone signal (meaning thata continuous sequence of 3 reciprocations meeting criteria have occurredwithin a specified interval) provides sufficient evidence to identify acluster object. When two simultaneous signals are processed, a totalscore of 4, derived from adding the number of reciprocations meetingcriteria in each signal, is sufficient to indicate the presence of acluster object. In this manner the cluster is continued by a sequentialunbroken count greater than 3 with one signal. or greater than 4 withtwo signals. Once the presence of a cluster object has been establishedalong the time series, at any point along the cluster object thesequential count along one signal can be converted to a continuation ofthe sequential count along another signal allowing the cluster object tocontinue unbroken. The failure of the occurrence of a cycle meetingcriteria within either signal within a specified interval (for exampleabout 90-120 seconds, although other intervals may be used) breaks thecluster object. A new cluster object is again identified if the countagain reaches the thresholds as noted above. It can be seen that thisscoring method takes into account the fact that artifact often affectsone signal and not another. Therefore if either signal alone provides asufficient score, the presence of a cluster object is established. Inaddition, the effect of brief episodes of artifact affecting bothsignals is reduced by this scoring method. In this way, artifact, unlessprolonged, may cause the cluster object to be broken but as soon as theartifact has reduced sufficiently in anyone or more signals the processof scoring for a new cluster object will restart. The skilled artisanwill recognize that many other such scoring or artifact rejectingmethods using two linked signals can be derived within the scope of thisteaching.

When applied, one preferred digital pattern recognition program forautomatically detecting, characterizing and indexing the severity airwayinstability proceeds in several phases, which need not be in theillustrative sequence, listed below:

1. Various types of decline and rise objects are identified at thecomposite level.

2. Various types of negative and positive reciprocations are identifiedat the composite level.

3. Various types of clusters of reciprocations are identified in thecomplex level.

4. Various types of reciprocations of clustered reciprocation objectcharacteristics (e.g. nadirs) are identified,

6. Reciprocations at the fundamental level are analyzed within specificobjects at the composite level.

7. Objects of one channel are compared to objects of another channel (ifmultiple time series of objects are rendered).

8. An expert system is applied to recognize a pattern of objectsindicative of a diagnosis.

9. The relationship between the events, reciprocations, and complexpatterns is calculated and outputted.

10. The severity of the pathophysiologic perturbation is determined

11. A textual alarm and/or other signal, which may be graded based onseverity is outputted

12. Treatment is automatically modified to adjust or prevent medicationinfusion or to eliminate the cluster

13. The process is then repeated with each addition to the dataset inreal-time or with stored timed datasets.

A mathematical representation of a basic iterative process of waveformobject segmentation according to the present invention is described indetail in the co-pending application entitled System and Method forIdentifying Dynamic Patterns of Interaction, the disclosure of which isincorporated by reference as if completely disclosed herein and anexemplary mathematical reference listing is provided in FIG. 20 a-20 c.These basic algorithms are particularly suited to identify monomorphicclusters and which, because of the precipitous nature of the operativepathophysiology driving the airway instability cycle, are largelycomprised of two basic types of events (unipolar rise and unipolarfall), and a basic type of reciprocation (a simple negative or positivereciprocation) and a single cluster type (as a collection of negative orpositive reciprocations). As will be shown many basic pathophysiologicprocesses are comprised of this type of simple reciprocations and thebasic embodiment suffices to characterizing them.

The present inventors have described clusters comprised ofreciprocations of varied morphology as polymorphic (an example of such acluster is shown in figure t Ob). For the purpose of the presentlypreferred embodiment, with respect to the oximetry time series, apolymorphic cluster is a cluster of reciprocations, which contains atleast two reciprocation morphologies and wherein the morphologies havedifferences other than simple variations in scale. In one embodiment,with respect to the oximetry time series, variation of the nadirs of thenegative reciprocations is considered still consistent with monomorphicclusters. However in this embodiment, negative reciprocations of thepeaks exceeding a 6% variation is considered indicative of a polymorphiccluster. Many modifications and different definitions can be applied todifferentiate polymorphic from monomorphic clusters. In one preferredembodiment, the morphology of different reciprocation types isselectable by selecting the characteristics of the events from whichthey are derived (the reciprocations inheriting these characteristics).A monomorphic cluster may degenerate into a polymorphic cluster, andagain this is recognized by identifying the presence of new types ofreciprocations or events developing along the cluster. For example theoccurrence of brief reciprocations with progressively falling peakscoupled with prolonged reciprocations with prolonged decline componentswould be one example of morphology of a polymorphic cluster. The presentinventors recognized that reciprocation morphology was a window into theintegrity and function of the control system. For this reason, theautomatic recognition, according to the present invention provides animportant function since the presence of a polymorphic cluster canindicate a control system which is highly unstable, severely attenuated,the presence of competing control systems (which can indicate partialattenuation of a primary controller and salvage by a secondarycontroller, or it can indicate that multiple pathophysiologic processare overlapping).

The present inventors have discover that, at least in one population ofpatients with airway instability, the basic monomorphic patterndominates and polymorphic patterns occur in less than 10% of thepatients. However, this may increase with narcotic administration orwith select subgroups of the severe obesity (at risk for obesityhypoventilation syndrome) or those with COPD, or CHF. The presentinventors have discovered that the automatic recognition of complexpolymorphic clusters can be useful to determine the stability andintegrity of ventilation control and the probability of success ofautomatic CPAP titration. This is an important discovery because primarycare physicians have been reluctant to institute automatic CPAP in thehome without attendance because the probability of success for a givenpatient cannot be known a priori. The present inventors recognized thatthe presence of polymorphic clusters and/or monomorphic clusterssuperimposed on other reciprocations (an occurrence which develops, forexample, when airway instability is superimposed on other cardiovascularand pulmonary co morbidities) have a lower probability of successfulautomatic CPAP titration and are therefore may best titrated in a sleeplaboratory or other attended setting where a trained attendant canadjust treatment. They also recognized that polymorphic reciprocationsare often best treated with multilevel ventilation because they oftenindicate an incomplete, or delayed recovery, which is often bestsupported by ventilation rather than simple CPAP.

According to the present invention, a automatic CPAP titration which canuse any of a range of algorithms for CPAP adjustment, as are known inthe art, upon recognition by the processor of polymorphic clusters, orthe presence of a decline in a time series of the peaks of clusters (asfor example in the oximetry signal), and/or the failure to abortclusters, the processor is programmed to automatically converts the CPAPto a bi level ventilator. According to the present invention, this canbe achieved by automatically lowering the pressure on exhalation by forexample 2-4 cm H2O and maintaining the original pressure on inhalation.Alternatively, the processor can trigger the addition of higher pressureon inspiration, for example 2 cm H2O, above the original continuouspressure level and then titration of the inspiratory pressure and orexpiratory pressure until the cluster is aborted. According to thepresent invention, if pathophysiologic divergence is identified with afall in oxygen saturation in relation to a rise in ventilation (asdiscussed in detail in the aforementioned co-pending application) thisrecognition can be used to automatically warn of the potential need foroxygen or to automatically initiate or increase oxygen instead ofincreasing the CPAP or Ventilation. Also the development ofpathophysiologic divergence in association with upward titration can beused to reduce the pressure to their original levels. Automaticconversion to ventilation or increasing ventilation upon the occurrenceof polymorphic clusters and/or when the cluster peaks exhibit asignificant decline or automatically aborting the titration and/orproviding an output indication that the patterns indicate that attendedtitration may be preferable provides a more physiologically focusedtitration of therapy which can substantially improve the efficacy andsale of automatic positive pressure treatment devices.

According to the present invention, a wide range of variations ofevents, reciprocations and clusters are built from simpler objects andthis provides the ability to identify a wide range of physiologicphenomena without losing information gained by aggregating specifictypes of such phenomena. For this reason the presently preferred timedomain analysis starts with a basic segmentation process upon which isbuilt a more complex characterization. The more complex physiologicoccurrences are better represented programmatically than mathematicallyand this representation follows. Because the wide range of permutationscauses marked complexity, the present inventors recognized that aneffective framework and methodology was required to address thecomplexity of this type of software system. In the presently preferredembodiment object oriented time series segmentation, in the time domain,into an inheritance based relational hierarchy allows recognition andcharacterization a very broad range of real-world phenomena includingcomplex, interactive physiological datasets.

The diagram of FIG. 11 represents both the real-world objects (e.g.reciprocations) and support objects for the rapid creation, rendering ofsuch object to a graphical user interface and persisting such objects torelational database and/or a hierarchical data store (e.g. eXtensibleMarkup Language (XML) documents). This diagram is only representativeand objects for accomplishing such rendering and persisting are notshown and may according to the present invention vary greatly dependingupon the operating environment of the application.

The core objects of the presently preferred analysis are shown in FIG.12. Here a case represents a single timed data set (such as 12 or 24hours halter monitoring period, one night in the sleep lab or hospital,or a variable post operative period) outputted from the monitoredpatient and may contain any number of channels. A channel is a singledata stream (e.g. oximetry). A time series (as described earlier) is aset of contiguous data points derived from a particular physiologicsignal (e.g. oximetry). As shown, a case is any number of channels and achannel contains a single time series. These raw data datasets are thenconverted (as described earlier) into objects of increasing complexityevents, reciprocations and clusters (of events and/or reciprocations).Each of these objects are representations of a segment of the wave (asdesignated by their reference to the WaveSegment object) but containmuch more information than simply the raw data as provided by theircontext within the analysis (their relationship to other objects). Itshould be noted that the relationship to predecessor objects ismaintained to provide for the layered complexity as described earlier.

Since the creation of hundreds of thousands of dipole objects can hamperthe acquisition of the analysis in a real-time environment, in thepresently preferred embodiment, the logical concept of the dipole objecthas been encapsulated within the time series to improve performance in areal time environment.

Correlated events provide the basic mechanism for identifyingrelationship between channels. The aggregation of these objects within acase analysis object provides the ability to interpret broad, intra andinter-channel trends while also providing access to the large amount ofsimple objects and raw data from which those trends were derived.according to the present invention this presently preferred structure inthe context of an object-oriented programming language that provides thepower to readily apply a broad range of expert system based comparisons,create indices, alarms, graphical representations and other human facingmechanisms for interpretation and analysis. FIG. 12 shows the basicrelationships of the objects created. In a completed analysispotentially thousands of such objects are created and related to eachother. Further, within each object a wide range of rich functionality isexposed. As an example, the time series object exposes the followingfunctions:

-   -   TimeSenesKey    -   SampleRate    -   UpperLimit    -   LawerLimit    -   NumberOfPoints    -   StartTime    -   CreateTime    -   StartPointPosition    -   EndPointPosition    -   Duration    -   EndTime    -   MeanValue    -   MaxValue    -   MmValue    -   ValueAtPoint    -   SumAtPoint    -   StartSequentialLoad    -   SequentialLoadPoint    -   TimeAtPoint    -   DipolesToTimeSpan    -   TinmeSpanToDipoles    -   TimeToNearestPoint    -   CompareTime    -   ComparePointPosition    -   DipolePointPosition1    -   DipolePointPosition2    -   DipoleDuration    -   NumberOfDipoles    -   DipoleChange    -   DipoleSlope    -   DipolePolarity    -   MeanValueOfSegment    -   MinValueOfSegment    -   MaxValueOfSegment    -   DipolePositionAfter    -   ExtractKey    -   PointsNeedToBeSaved

As a further example, the following is a list of functions available fora cluster object (this does not include the ability of that object toaccess any functionality of the reciprocations, events and time serieswhich are related to the cluster object):

-   -   NumberOfPoints    -   NumberOfDipoles    -   StartPointPosition    -   EndPointPosition    -   StartDipolePosition    -   EndDipolePosition    -   StartTime    -   Duration    -   EndTime    -   MinValue    -   MaxValue    -   MeanValue    -   ChangeAccordingToEndPoints    -   SlopeAccordingToEndPoints    -   CompareTime    -   ComparePointPosition    -   ClusterType    -   ClusteredReciprocations    -   MeanStartEventDuration    -   MeanEndEventDuration    -   MeanStartEventMaginitude    -   MeanEndEventMagnitude    -   MennStartEventSlope    -   MeanEndEventSlope    -   MeanReciprocationMaxValue    -   MeanReciprocationMinValue    -   MeanReciprocationMagnitude    -   MeanReciprocationDurationRatio    -   MeanReciprocationMalagnitudeRatio    -   MeanReciprocationSlopeRatio    -   MeanRecoveryDuration    -   MeanRecoveryRatio( )

FIG. 13 shows the preferred support mechanisms for the creation of theanalysis. In the presently preferred embodiment the analysis definitionis separated from the analysis itself allowing for the efficientproliferation of patient data and analysis while maintaining a small setof metadata. Although the analysis and transforms may be fixed forapplication in the hospital, this flexibility allows researchers theability to create any number of analysis definitions to apply to variouspatient types, and/or disorders.

As shown, in the above structure a requesting system will create ananalysis request and submit it to the case analysis director. Thedirector is a dispatcher object that oversees the analysis process whiledelegating the actual processing to other objects. The most important ofthese delegates is the channel analysis builder. This object performsthe basic algorithms described above against the respective time seriesto produce the aforementioned events, reciprocations, clusters and therelationships between them.

During this process data structures for providing fast access by pointand dipole are created as point and dipole maps respectively. Theseobjects provide single step access to all objects tied to a particularpoint and/or dipole. This supports a high level of interactivity (e.g.mouse over actions) in a graphic user interface (e.g. a MicrosoftWindows application).

Analysis requests may not be comprehensive and therefore furtheranalysis can be requested to the case analysis director. This ability toprovide partial analysis further enhances performance by allowing aresearcher or physician to specify the particular aspects of theanalysis he/she finds useful.

The channel integrator performs intra-channel analysis to create eventcorrelations. These correlations (and the aggregations thereof) providean integrated view of all channels within a case.

FIG. 14 shows one preferred data acquisition and transformation systemfor preceding the analysis. The time series acquisition andtransformation system of FIG. 14 provides for a flexible interface(real-time and otherwise) to data providers and data stores. The importand transform subsystems follow a similar design pattern of a request, adirector and a set of delegate objects to perform the requiredoperations. The present embodiment allows researchers the ability toreadily transform (e.g. filter, smooth, integrate) the time seriesbefore performing analysis. The results of these transformations canthen become a channel within the case itself on which analysis can beperformed in the same aforementioned way. These channels can be, forexample. A real-time time series of a calculated index of two or moresignals such as airflow and oximetry to quickly identifypathophysiologic divergence of these normally linked parameters inpostoperative patients or those at risk for pulmonary embolism orsepsis. In another example the new channel can be a real-timeintegration of oximetry and pulse to improve automatic severity indexingduring monitoring for neonatal apnea).

Of course, the transformation itself can be a quite complex process andthe transform has a substantial impact on the waveform morphology andrelationships. This has been a significant problem with standardhospital monitoring systems (such as pulse oximeters). Despite theimportance of the effect a transform has on time series morphology andalarm reliability the monitoring industry has not standardized to anytransform and many oximeters do not even provide documentation relevantthe transform applied to generate the time series. The present inventorsrecognized that, in the interest of providing optimal patient care, thetransform needs to be understood by the physician and researcher so thatthe time series outputs and all algorithms performed are clearlyidentified and/or can be explicitly requested. For this purpose, thepresent inventors created a nomenclature for explicit representation ofan transformations available.

FIG. 17 shows one presently preferred nomenclature for the exemplaryparameters of airflow, pulse, and oxygen saturation. Illustrativeexamples of designation by this nomenclature are shown in FIG. 18.

According to the present invention, objects at the composite levelencapsulate the objects from which they are derived at the fundamentallevel. For this reason a recovery object recognized at the compositelevel in one parameter can used to specify a region for comparison ofsequential objects (such as reciprocations) at the fundamental objectlevel in the same parameter or in matched recovery objects along anotherparameter. For example, upon recognition of the presence of a recoveryobject (where. it is anticipated that the ventilation effort will behigh) the ratio of the slope off all (exhalation) objects and rise(inhalation) objects at the fundamental level can be compared within therecovery object and the time series derived from an continuouscalculation this ratio can be plotted at it too analyzed by objectmapping if desired. During upper airway obstruction, the inspiration isslowed to a greater degree than exhalation. The magnitude change ofinspiratory slowing and/or amplitude and/or ratio during the clusters ofapneas provides an index of the magnitude of upper airway narrowing(which slows inhalation during the clustered apnea/hypopnea objects).However, during the recovery object or at the “hyperventilationreference point”, the upper airway should be wide open for bothinhalation and exhalation and this can be used as a reference because,during this time. Because the recovery objects encapsulate thefundamental reciprocation objects from which it is derived, the absoluteslope, magnitude, frequency and amplitude, of the fundamental objectsduring recovery can then be compared to provide a reference for theabsolute slope magnitude, frequency and amplitude of the fundamentalobjects during other times along the night to provide an indication ofupper or lower airway narrowing.

These encapsulations allow ready exploitation that certain regions alonga multi-signal object (as within an airway instability cluster) have avery high probability of association with various levels of ventilation.According to the present invention, the objects defining those regionscan then be used as a reference or as an opportunity to examine for theeffects of a given level of ventilation effort on the flowcharacteristics of the encapsulated reciprocations. Patients withobstructive sleep apnea are expected to have a hill in the slopes offundamental inspiration objects during decline objects at the compositelevel indicative of upper airway narrowing and/or occlusion. Also, asshown in FIG. 7, patients with asthma or chronic obstructive lungdisease may have a reduced slope of the exhalation when compared to theslope of inhalation during the rise objects between apneas at the baselevel. According one embodiment of the present invention, the ratio ofthe slope of inhalation objects at the fundamental level is compared tothe slope of the exhalation objects at the fundamental level and thiscan be plotted as a time series for object based analysis. Patients withsimple, uncomplicated obstructive apnea will have clusters of increasingslope ratios with the ratio rising to about one during the recoveryobjects. Patients with combined obstructive apnea and asthma or chronicobstructive lung disease will have increased ratios during the recoveryobjects into the range of 2-3 or greater, indicating the development ofobstructive lower airways during the rapid breathing associated withrecovery.

Another example of object processing at the fundamental object level,according to the present invention, includes the processor-basedidentification of fluttering of the plateau on the pressure signal torecognize partial upper airway obstruction. During the nasal pressuremonitoring a fluttering plateau associated with obstructive breathingoften occurs intervening a rise event and a fall event of tidalbreathing. Since the plateau objects are easily recognizable at thefundamental level and readily separated using the present objectrecognition system the plateau can be processed for the tiny rise andfall objects associated with fluttering and the frequency of theseobjects can be determined. Alternatively, a Fourier transform can beapplied to the plateau objects between the rise and fall events of thenasal pressure signal to recognize the presence of fluttering or anothermethod can be utilized which provides an index of the degree offluttering of the plateau objects.

Since reduced effort also lowers the slope of exhalation andinspiration, the configuration (as defined by the slope dataset of thedipoles defining the fundamental objects of both inspiration andexpiration at the reference objects) can be applied as referencefundamental object configurations defining the presence ofhyperventilation or hypopnea. This process is similar to the selectionprocess for identifying search objects described earlier but in thiscase the input region is pre-selected. In an example, the range ofcharacteristics of the objects at the fundamental level derived from oneor more tidal breaths occurring prior to the second airflow delta pointcan be used to designate a reference hyperventilation objects range.Alternatively the object based characteristics, defined by of the rangeof characteristics of the objects derived from one or more tidal breathsoccurring prior to the first airflow delta point can be used designate areference hypopnea objects range. The processor can then automaticallyassess object ranges along other points of the tracing. In this way theprocessor can apply an artificial intelligence process to theidentification of hypopneas by the following process:

-   -   1. Identify the region wherein a hypopnea is expected (as for        example two to three tidal breaths prior to the first airflow        delta point).    -   2. Select this as a region for objects processing to define the        characteristics of hypopneas in this patient.    -   3. Process the region using the slope dipole method to define        the range of fundamental objects comprising the target region.    -   4. Compare the identified range of objects to other analogous        objects along to tracing to identify new objects having similar        characteristics.    -   5. Using the criteria derived from the objects defining the        target region search the processed waveform for other regions        having matching sequences of new objects and identifies those        regions.    -   6. Provide an output based on said identification and/or take        action (e.g. increase CPAP) based on said identification.

In one embodiment, the multi-signal time series output is placed into aformat particularly useful for demonstrating events to hospital personalespecially for teaching purposes. In this format the output controls ananimation of multiple objects which are shaped into an animatedschematic of the as the physiologic system being monitored. Theanimation moves over time and in response to the signals and onepreferred embodiment the type of signals (or the reliability of suchsignals) determines which components of the schematic are “turned on”and visible. One example includes a multi-signal object defined byoutputs of airflow, thoracic impedance, oximetry, and blood pressurerendering set of a connected set animation objects for the lungs, upperairway, lower airway, heart, and blood vessels which can be animated as;

-   -   Each inspiration causing an animated enlargement of the lungs        tracking the inspiration slope,    -   Each expiration causing an animated reduction in size of the        lungs tracking the expiration slope,    -   Each animated systolic beat of the heart tracks the QRS or        upstroke of the oximetry output,    -   The color of the blood in the arteries and left heart tracks the        oxygen saturation,    -   The diameter of the lower airway (a narrowing diameter can be        highlighted in red) tracks the determination of obstruction by        the slope ratio in situations of hyperventilation (as discussed        previously),    -   The patency of the upper airway (a narrowing or closure can be        highlighted in red) tracks the determination of upper airway        obstruction (for example the airway is shown as opening and        closing in clusters when ventilation effort (as by chest wall        movement) is identified in clusters with absent nasal flow.

This provides “physiologic animation” which can be monitored inreal-time and which can also be derived and reviewed from the storedmulti-signal objects at variable time scales. This embodiment of thepresent invention provides a quickly, easily understood and dynamicanimated output of a highly complex, interactive time series derivedfrom a patient. The animation can be reviewed at an increased timelapsed to speed through evolution of a given patients outputs or can beslowed or stopped to see the actual global physiologic state at thepoint of onset or termination of a given pathophysiologic perturbation.

Particularly for use in the hospital, a graded indicator or alarm can beprovided, indicative of the severity of the clusters and of air way orventilation instability. Since very mild clustering may simply representthe effect of moderate sedation, and not, therefore, represent a causefor great concern (although it is important to recognize that it ispresent). Such a clustering could be identified with a single bar ortest warning, whereas more severe clustering would generate a largerwarning and, if very severe, an auditory alarm. When the clusteringbecomes more severe and demonstrates greater levels of desaturationand/or shorter recovery intervals the bar can be doubled.

According to another aspect of the present invention, a change in one ormore time series components of the multi-signal object can be used tochange the processing algorithm of a time series component of themulti-signal object. In an example, the recognition of airwayinstability is enhanced by improved fidelity of the timed waveform (aswith pulse oximetry). FIG. 19 shows one preferred method, according tothe present invention, of improving the general fidelity of the entiretimed waveform of S_(p)O₂ for enhanced pattern & cluster recognition inan environment where the patient, at times, has motion and, at othertimes, does not. It is optimal, for example, in monitoring oximetry forthe probe to be placed on a portion of the patient, which is notassociated with motion. However, in most cases, this is unrealistic andmotion is commonly associated with routine clinical oximetry monitoring.It is well known that motion results in a fail in the saturation value,which is generated by the oximeter. Multiple theories for the cause ofthe fall have been promulgated. Several corporations, including Masimo,and Nellcor had developed algorithms, which can be used to mitigate theeffect of motion on the accuracy of the output. However, such algorithmscan include a significant amount of signal averaging, generally fourseconds or more. This can result in significant smoothing of thewaveform and reduces the fidelity of the waveform. Furthermore, itattenuates patterns of minor desaturations, which can be indicative ofairway instability, and clusters of hypopneas associated with variationsin airway resistance. As discussed in the aforementioned patents andpatent application, even minor desaturations when occurring in clusterscan be strong evidence for airway or ventilation instability and it isimportant to recognize such desaturations. Unfortunately, averagingintervals, especially those exceeding four seconds or more can result inattenuation of these desaturations and, therefore, hide these clustersso that the airway instability may not be recognized. However, motionitself results in artifact, which can simulate desaturations. Althoughsuch artifact is not expected to occur in typical cluster pattern, thepresence of motion artifacts significantly reduces the value of thesignal as an index of oxygen saturation and airway instability.

The present invention thereby provides for more optimal continuousfidelity of the waveform through both motion and non-motion states. Asillustrated in FIG. 16, when the motion time series output suggests thatsubstantial motion is not present, such as deep sleep or sedation,wherein the extremity is not moving, long averaging smoothing algorithmsor motion mitigation algorithms are not applied to the oxygen saturationand plethesmographic pulse time series. In the alternative, if theseries indicates motion then these motion mitigation algorithms areapplied. The variable application of averaging based on identificationof the absence or presence of motion provides optimal fidelity of thewaveform for monitoring airway instability.

Those skilled in the art will recognize that, the information providedfrom the data and analysis generated from the above-described system canform the basis for other hardware and/or software systems and has widepotential utility. Devices and/or software can provide input to or actas a consumer of the physiologic signal processing system of the presentinvention's data and analysis.

The following are examples of presently preferred ways that the presentphysiologic signal processing system can interact with other hardware orsoftware systems:

-   -   1. Software systems can produce data in the form of a waveform        that can be consumed by the physiologic signal processing        system.    -   2. Embedded systems in hardware devices can produce a real-time        stream of data to be consumed by the physiologic signal        processing system.    -   3. Software systems can access the physiologic signal processing        system representations of populations of patients for        statistical analysis.    -   4. Software systems can access the physiologic signal processing        system for conditions requiring hardware responses (e.g.        increased pressure in a CPAP device), signal the necessary        adjustment and then analyze the resulting physiological response        through continuous reading of the physiologic signal processing        system data and analysis.

It is anticipated that the physiologic signal processing system will beused in these and many other ways. To facilitate this anticipatedextension through related hardware and software systems the presentsystem will provide an application program interface (API). This API canbe provided through extendable source code objects, programmablecomponents and/or a set of services. Access can be tightly coupledthrough software language mechanisms (e.g. a set of C++ modules or Javaclasses) or proprietary operating system protocols (e.g. Microsoft'sDCOM, OMG's CORBA or the Sun Java Platform) or can be loosely coupledthrough industry standard non-proprietary protocols that providereal-time discovery and invocation (e.g. SOAP [Simple Object AccessProtocol] or WSDL [Web Service Definition Language]).

In the preferred embodiment the physiologic signal processing systemwith the API as defined becomes a set of programmable objects providinga feature-rich development and operating environment for future softwarecreation and hardware integration.

FIG. 19 a-19 e presents one presently preferred programming code forintegrating a plurality of signals and identifying dynamic patterns ofinteraction. The program is particularly suited for physiologic signalsbut can be used to track and identify relationships between a broadrange of signals to identify specific patterns or to search forpatterns. Although the listed program is coded for contiguous points forefficiency, according to the present invention, the time series datapoints need not be contiguous and indeed as discussed with physiologicdata sets, noncontiguous points are converted into a time series whennadirs or peaks of reciprocations are plotted to identify timed patternsof variation in these parameters. The present invention is applicable todetect dynamic patterns and relationships particularly in the timedomain in the along and between one or a plurality of financial timeseries such as stock prices or stock indexes, vibration time series,time series of sound, air movement, temperature, populations, and otherdynamic processes where it may be desirable to identify, along complexdata sets, known or unknown dynamic patterns of interaction.

Although the presently preferred embodiments have been described, whichrelate to the processing of physiologic signals, it is also critical torecognize the present streaming parallel objects based data organizationand processing method can be used to order and analyze a wide range ofdynamic patterns of interactions across a wide range of correspondingsignals and data sets in many environments. The invention is especiallyapplicable to the monitoring of the variations or changes to a physicalsystem, biologic system, or machine subjected to a specific process orgroup of processes over a specific time interval.

Many other additional parameters may be added and will become evident tothose skilled in the art in association with the application of thepresent invention and these are included within the scope of thisinvention. Those skilled in the art that various changes andmodifications can be made without departing from the invention. Whilethe invention has been described in connection with what is presentlyconsidered to be the most practical and preferred embodiments, it is tobe understood that the invention is not to be limited to the disclosedembodiments, but on the contrary, is intended to cover variousmodifications and equivalent arrangements included within the spirit andscope of the appended claims.

The invention claimed is:
 1. A monitoring system, comprising: a monitorconfigured for receiving input relating to patient physiological pulseoximetry or exhaled CO2 parameters and storing data related to theparameters, the monitor comprising: a processor programmed for analyzingthe data to detect and display an SPO2, a pulse, or an exhaled CO2pattern and to determine a severity of said pattern, the patterncomprising clustering of reciprocations of SPO2, pulse, or exhaled CO2data due to corresponding clustering of sleep apnea events; and at leastone processor programmed for communicating with a drug infusion pumpcontroller to discontinue drug infusion responsive to the severity ofsaid pattern.
 2. The system of claim 1, wherein data related to theparameters comprises pulse oximetry data.
 3. The system of claim 1,wherein the pattern comprises an oxygen saturation pattern indicative ofventilatory instability.
 4. The system of claim 1, further comprising agraphical indicator that changes in relation to the occurrence or theseverity of said pattern.
 5. A method, comprising: receiving inputrelating to patient physiological parameters and storing data related tothe parameters; detecting and displaying, with a processor, an oxygensaturation, a pulse, or an exhaled CO2 pattern, the pattern comprisingclustering of reciprocations of the oxygen saturation, the pulse, or theexhaled CO2 due to corresponding clustering of sleep apnea eventsindicative of ventilatory instability; determining, with the processor,a severity of the pattern; and communicating, via the processor, with adrug infusion pump controller to discontinue drug infusion responsive inreal-time or near real time, at least in part, to said determining of aseverity of said pattern.
 6. A system, comprising: a sensor configuredfor sensing patient physiological parameters comprising pulse oximetryor exhaled CO2 parameters; a monitor programmed for receiving input datafrom the sensor related to the patient physiological parameters andstoring the data related to the parameters, the monitor comprising: apattern detection and display feature programmed for analyzing the datato detect and display a pattern in the data, the pattern comprisingclusters of reciprocations of SPO2, pulse, or exhaled CO2 due tocorresponding cluster of sleep apnea; and at least one processorprogrammed for communicating with a drug infusion pump controller todiscontinue drug infusion responsive to said detecting of said pattern.7. The system of claim 6, wherein the sensor comprises a pulse oximetrysensor.
 8. A monitoring system, comprising: a monitor for receivinginput data relating to patient physiological parameters and storing thedata related to the patient physiological parameters, the monitorcomprising; a processor comprising a pattern detection and displayfeature programmed for analyzing and displaying the data to detect anddisplay a pattern in the data, the pattern comprising a cluster ofreciprocations, the reciprocations being defined by a fall, a nadir, arise, and a rise peak; a pattern qualification feature programmed fordetermining if the detected pattern is due to a ventilatory instability;and at least one processor for communicating with a drug infusion pumpcontroller to discontinue drug infusion responsive to said determining.9. The monitoring system of claim 8 wherein the data comprises data froma pulse oximeter or an exhaled CO2 measurement device, wherein thepattern qualification feature comprises a pattern detection featurecapable of detecting clustering of reciprocations of SPO2, pulse, orexhaled CO2 due to corresponding clustering of sleep apnea events.